3 citations · 7 across the 18 of their papers we have counts for
22 papers
Personalizing MLLMs via Reinforced Multimodal Reference Game
Deepayan Das, Davide Talon, Yiming Wang +2
Personalizing Multimodal Large Language Models (MLLMs) aims to recognize users' unique concepts from visual data and provide personalized responses. Although prior work has shown t…
Is SAM3 ready for pathology segmentation?
Qiuyu Kong, Shakiba Sharifi, Yiming Wang +2
Is Segment Anything Model 3 (SAM3) capable in segmenting Any Pathology Images? Digital pathology segmentation spans tissue-level and nuclei-level scales, where traditional methods…
EAR: Enhancing Uni-Modal Representations for Weakly Supervised Audio-Visual Video Parsing
Huilai Li, Xiaomeng Di, Ying Xing +3
Weakly supervised Audio-Visual Video Parsing (AVVP) aims to recognize and temporally localize audio, visual, and audio-visual events in videos using only coarse-grained labels. Fac…
Motion-Guided Semantic Alignment with Negative Prompts for Zero-Shot Video Action Recognition
Yiming Wang, Frederick W. B. Li, Jingyun Wang
Zero-shot action recognition is challenging due to the semantic gap between seen and unseen classes. We present a novel framework that enhances CLIP with disentangled embeddings an…
Specificity-aware reinforcement learning for fine-grained open-world classification
Samuele Angheben, Davide Berasi, Alessandro Conti +2
Classifying fine-grained visual concepts under open-world settings, i.e., without a predefined label set, demands models to be both accurate and specific. Recent reasoning Large Mu…
Training-free Online Video Step Grounding
Luca Zanella, Massimiliano Mancini, Yiming Wang +2
Given a task and a set of steps composing it, Video Step Grounding (VSG) aims to detect which steps are performed in a video. Standard approaches for this task require a labeled tr…
ConViS-Bench: Estimating Video Similarity Through Semantic Concepts
Benedetta Liberatori, Alessandro Conti, Lorenzo Vaquero +3
What does it mean for two videos to be similar? Videos may appear similar when judged by the actions they depict, yet entirely different if evaluated based on the locations where t…
Can Text-to-Video Generation help Video-Language Alignment?
Luca Zanella, Massimiliano Mancini, Willi Menapace +3
Recent video-language alignment models are trained on sets of videos, each with an associated positive caption and a negative caption generated by large language models. A problem…
On Large Multimodal Models as Open-World Image Classifiers
Alessandro Conti, Massimiliano Mancini, Enrico Fini +3
Traditional image classification requires a predefined list of semantic categories. In contrast, Large Multimodal Models (LMMs) can sidestep this requirement by classifying images…
Training-Free Personalization via Retrieval and Reasoning on Fingerprints
Deepayan Das, Davide Talon, Yiming Wang +2
Vision Language Models (VLMs) have lead to major improvements in multimodal reasoning, yet they still struggle to understand user-specific concepts. Existing personalization method…
One VLM to Keep it Learning: Generation and Balancing for Data-free Continual Visual Question Answering
Deepayan Das, Davide Talon, Massimiliano Mancini +2
Vision-Language Models (VLMs) have shown significant promise in Visual Question Answering (VQA) tasks by leveraging web-scale multimodal datasets. However, these models often strug…
Retrieval-enriched zero-shot image classification in low-resource domains
Nicola Dall'Asen, Yiming Wang, Enrico Fini +1
Low-resource domains, characterized by scarce data and annotations, present significant challenges for language and visual understanding tasks, with the latter much under-explored…
Automatic benchmarking of large multimodal models via iterative experiment programming
Alessandro Conti, Enrico Fini, Paolo Rota +3
Assessing the capabilities of large multimodal models (LMMs) often requires the creation of ad-hoc evaluations. Currently, building new benchmarks requires tremendous amounts of ma…
Vocabulary-free Image Classification and Semantic Segmentation
Alessandro Conti, Enrico Fini, Massimiliano Mancini +3
Large vision-language models revolutionized image classification and semantic segmentation paradigms. However, they typically assume a pre-defined set of categories, or vocabulary,…
Test-Time Zero-Shot Temporal Action Localization
Benedetta Liberatori, Alessandro Conti, Paolo Rota +2
Zero-Shot Temporal Action Localization (ZS-TAL) seeks to identify and locate actions in untrimmed videos unseen during training. Existing ZS-TAL methods involve fine-tuning a model…
Harnessing Large Language Models for Training-free Video Anomaly Detection
Luca Zanella, Willi Menapace, Massimiliano Mancini +2
Video anomaly detection (VAD) aims to temporally locate abnormal events in a video. Existing works mostly rely on training deep models to learn the distribution of normality with e…
Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
Victor G. Turrisi da Costa, Nicola Dall'Asen, Yiming Wang +2
Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers…
Collaborative Neural Painting
Nicola Dall'Asen, Willi Menapace, Elia Peruzzo +3
The process of painting fosters creativity and rational planning. However, existing generative AI mostly focuses on producing visually pleasant artworks, without emphasizing the pa…
Novel class discovery meets foundation models for 3D semantic segmentation
Luigi Riz, Cristiano Saltori, Yiming Wang +2
The task of Novel Class Discovery (NCD) in semantic segmentation entails training a model able to accurately segment unlabelled (novel) classes, relying on the available supervisio…
Geometrically-driven Aggregation for Zero-shot 3D Point Cloud Understanding
Guofeng Mei, Luigi Riz, Yiming Wang +1
Zero-shot 3D point cloud understanding can be achieved via 2D Vision-Language Models (VLMs). Existing strategies directly map Vision-Language Models from 2D pixels of rendered or c…
Delving into CLIP latent space for Video Anomaly Recognition
Luca Zanella, Benedetta Liberatori, Willi Menapace +3
We tackle the complex problem of detecting and recognising anomalies in surveillance videos at the frame level, utilising only video-level supervision. We introduce the novel metho…
LCReg: Long-Tailed Image Classification with Latent Categories based Recognition
Weide Liu, Zhonghua Wu, Yiming Wang +4
In this work, we tackle the challenging problem of long-tailed image recognition. Previous long-tailed recognition approaches mainly focus on data augmentation or re-balancing stra…